docs/tutorials/knefmnn_general_use.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enhanced Online Learning Algorithm with K-nearest Hyperboxes Selection for FMNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to use the Simpson's Fuzzy Min-Max Neural Network classifier using an enhanced online learning algorithm with k-nearest hyperboxes selection (KNEFMNN)\n",
    "\n",
    "Note that the numerical features in training and testing datasets must be in the range of [0, 1] because the KNEFMNN classifier require features in the unit cube."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Execute directly from the python file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the path to the this jupyter notebook file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\docs\\\\tutorials'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_notebook_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
    "this_notebook_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the home folder of the Hyperbox-Brain project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/hyperbox-brain')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "project_dir = Path(this_notebook_dir).parent.parent\n",
    "project_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the path to the Python file containing the implementation of the Simpson's FMNN classifier using the enhanced online learning algorithm with k-nearest hyperboxes selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\hbbrain\\\\numerical_data\\\\incremental_learner\\\\knefmnn.py'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knefmnn_file_path = os.path.join(project_dir, Path(\"hbbrain/numerical_data/incremental_learner/knefmnn.py\"))\n",
    "knefmnn_file_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the found file by showing the execution directions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: knefmnn.py [-h] -training_file TRAINING_FILE -testing_file TESTING_FILE\n",
      "                  [--theta THETA] [--gamma GAMMA] [--k_neighbors K_NEIGHBORS]\n",
      "                  [--is_draw IS_DRAW]\n",
      "\n",
      "The description of parameters\n",
      "\n",
      "required arguments:\n",
      "  -training_file TRAINING_FILE\n",
      "                        A required argument for the path to training data file\n",
      "                        (including file name)\n",
      "  -testing_file TESTING_FILE\n",
      "                        A required argument for the path to testing data file\n",
      "                        (including file name)\n",
      "\n",
      "optional arguments:\n",
      "  --theta THETA         Maximum hyperbox size (in the range of (0, 1])\n",
      "                        (default: 0.5)\n",
      "  --gamma GAMMA         A sensitivity parameter describing the speed of\n",
      "                        decreasing of the membership function in each\n",
      "                        dimension (larger than 0) (default: 1)\n",
      "  --k_neighbors K_NEIGHBORS\n",
      "                        The number of nearest hyperboxes is considered for the\n",
      "                        hyperbox expansion process\n",
      "  --is_draw IS_DRAW     Show the existing hyperboxes during the training\n",
      "                        process on the screen (default: False)\n"
     ]
    }
   ],
   "source": [
    "!python \"{knefmnn_file_path}\" -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the path to training and testing datasets stored in the dataset folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\dataset\\\\syn_num_train.csv'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_file = os.path.join(project_dir, Path(\"dataset/syn_num_train.csv\"))\n",
    "training_data_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\dataset\\\\syn_num_test.csv'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data_file = os.path.join(project_dir, Path(\"dataset/syn_num_test.csv\"))\n",
    "testing_data_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run a demo program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hyperboxes = 51\n",
      "Testing accuracy =  86.60%\n"
     ]
    }
   ],
   "source": [
    "!python \"{knefmnn_file_path}\" -training_file \"{training_data_file}\" -testing_file \"{testing_data_file}\" --theta 0.1 --k_neighbors 5 --gamma 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Using the KNEFMNN classifier through its init, fit, and predict functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hbbrain.numerical_data.incremental_learner.knefmnn import KNEFMNNClassifier\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create training and testing data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(training_data_file, header=None)\n",
    "df_test = pd.read_csv(testing_data_file, header=None)\n",
    "\n",
    "Xy_train = df_train.to_numpy()\n",
    "Xy_test = df_test.to_numpy()\n",
    "\n",
    "Xtr = Xy_train[:, :-1]\n",
    "ytr = Xy_train[:, -1]\n",
    "\n",
    "Xtest = Xy_test[:, :-1]\n",
    "ytest = Xy_test[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initializing parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = 0.1\n",
    "k_neighbors = 5\n",
    "gamma = 1\n",
    "is_draw = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "    this.context = canvas[0].getContext(\"2d\");\n",
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       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"551.3333333333334\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KNEFMNNClassifier(C=array([1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1,\n",
       "       1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2,\n",
       "       1, 2, 2, 2, 2, 2, 1]),\n",
       "                  V=array([[0.42413   , 0.53516   ],\n",
       "       [0.70577   , 0.397105  ],\n",
       "       [0.82785   , 0.78025   ],\n",
       "       [0.66038   , 0.51128   ],\n",
       "       [0.48794   , 0.672     ],\n",
       "       [0.26651   , 0.18424   ],\n",
       "       [0.32289   , 0.59994   ],\n",
       "       [0.19944   , 0.03      ],\n",
       "       [0.28077   , 0.26124   ],\n",
       "       [0.63683   , 0.6936    ],\n",
       "       [0.28822   , 0.55512   ],\n",
       "       [0.03      , 0.47757   ],\n",
       "       [0...\n",
       "       [0.91185   , 0.5761    ],\n",
       "       [0.2246    , 0.13567   ],\n",
       "       [0.25929   , 0.81558   ],\n",
       "       [0.815     , 0.397095  ],\n",
       "       [0.67906   , 0.83605   ],\n",
       "       [0.52197   , 0.91371   ],\n",
       "       [0.66037   , 0.57837   ],\n",
       "       [0.49408   , 0.66846   ],\n",
       "       [0.80583   , 0.43242   ],\n",
       "       [0.79935   , 0.7757    ],\n",
       "       [0.35813   , 0.58772   ],\n",
       "       [0.79516   , 0.32629   ],\n",
       "       [0.36057   , 0.71561   ],\n",
       "       [0.68469   , 0.29485   ],\n",
       "       [0.70743   , 0.50325   ],\n",
       "       [0.25621   , 0.62174   ],\n",
       "       [0.14737   , 0.28498   ],\n",
       "       [0.56487   , 0.17003   ],\n",
       "       [0.55763   , 0.43813   ]]),\n",
       "                  is_draw=True, theta=0.1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knefmnn_clf = KNEFMNNClassifier(theta=theta, k_neighbors=k_neighbors, gamma=gamma, is_draw=is_draw)\n",
    "knefmnn_clf.fit(Xtr, ytr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The code below shows how to display decision boundaries among classes if input data are 2-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"608.6666666666666\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knefmnn_clf.draw_hyperbox_and_boundary(\"The trained KNEFMNN classifier and its decision boundaries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of existing hyperboxes = 51\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of existing hyperboxes = %d\"%(knefmnn_clf.get_n_hyperboxes()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy =  86.60%\n"
     ]
    }
   ],
   "source": [
    "y_pred = knefmnn_clf.predict(Xtest)\n",
    "acc = accuracy_score(ytest, y_pred)\n",
    "print(f'Accuracy = {acc * 100: .2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explaining the predicted result for the input sample by showing membership values and hyperboxes for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_need_explain = 10\n",
    "y_pred_input_0, mem_val_classes, min_points_classes, max_points_classes = knefmnn_clf.get_sample_explanation(Xtest[sample_need_explain])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class for sample X = [0.571640, 0.233700] is 2 and real class is 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted class for sample X = [%f, %f] is %d and real class is %d\" % (Xtest[sample_need_explain, 0], Xtest[sample_need_explain, 1], y_pred_input_0, ytest[sample_need_explain]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Membership values:\n",
      "Class 1 has the maximum membership value = 0.964263\n",
      "Class 2 has the maximum membership value = 0.990050\n",
      "Class 1 has the representative hyperbox: V = [0.58339 0.3649 ] and W = [0.66091    0.38616125]\n",
      "Class 2 has the representative hyperbox: V = [0.57285 0.27229] and W = [0.66773 0.36489]\n"
     ]
    }
   ],
   "source": [
    "print(\"Membership values:\")\n",
    "for key, val in mem_val_classes.items():\n",
    "    print(\"Class %d has the maximum membership value = %f\" % (key, val))\n",
    "    \n",
    "for key in min_points_classes:\n",
    "    print(\"Class %d has the representative hyperbox: V = %s and W = %s\" % (key, min_points_classes[key], max_points_classes[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show input sample and hyperboxes belonging to each class. In 2D, we can show rectangles or use parallel coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using rectangles to show explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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Jce8Zy8WSu64445y7rnnliuuuKKr+Zvf/KasttpqZYcddujOcK2//voveI0a1D796U+Xb33rW11oW3rppcuECRPKNttsUw455JDy+te/ftY2P/3pT8unPvWpcu2115Zf/epXZfnlly9rrLFG2X777bszbHW70R6R+z4SqH7+85+X448/vnvvdb9e9apXlb322qsceuih3fuZ+/H000+Xz3/+893zf/azn5V6JrCuC/7+7/++vP/973/Bl7n6DNVoXV10/r7Xn8XWkzm9F6hyXMOrGoBwUgUJEBhAoNdf4gO8lE2HRODDH/5wOe2008rBBx9cTjrppJ726sVCyT/+4z+WE088sWywwQblta99bRcm6k0uahBaYYUVyjXXXFM23HDDWa/1y1/+smyyySblkUce6f59vQnGjBkzuhsw1MsRv/zlL8+6zPCmm27qQlb9+y233LKr/8QTT5Rf/OIXXf2rrrqqC1ajPaL2vb5ODVT1Zh31TF8NhDvuuGN5yUteUr797W93n0Gr/1zD5RJLLDFrt+oNQGqQrRb1S7Q333zzsvjii5frrruuuxlIDVVf+MIX5ngbAtVoXV10/r7Xn8XWkzm9F6hyXMOrGoBwUgUJEBhAoNdf4gO8lE2HRKCGk+9973vlK1/5Snc2pZfHi4WSeoOLVVddtayzzjpzlKt3A6xnXt785jeX73znO7P+bqTOZz/72e5s1OyPGqqeeeaZWbXq3QTPOeeccuGFF3Y30Zj9UQPViiuuONAZql73fSRQ1f+tZ/muvvrq8rrXva7brXojj3pW7sc//nE59dRTy0c+8pFZu/vBD36wOzv1vve9r/yf//N/ynLLLTdrm912261MnTq1uwHILrvsMmsbgaqXo3N8P7fXn8XWkzn9FqhyXMOrGoBwUgUJEBhAoNdf4gO8lE2HROC//bf/1l1uVi/9e9vb3tbTXvXzOaQa4OqlevUszMtf/vLu9UbCxQ9+8IOy8cYbz3cf/uIv/qJcdtll3fY1PPX7iNr32QNVveX8AQccMMcujVxSWS9zvO2227q/e+ihh7rwVf/UEFjvnjj74+abb+4carCqt7AfeQhU/XZ7/G3X689i68mcHgtUOa7hVQ1AOKmCBAgMINDrL/EBXsqmQyJQL6+rC/3oQPX73/++fP3rXy8//OEPu89QzZw5s3vH9RK3ennejTfeWDbddNPu35155pndd1xtvfXW5bjjjisTJ0580c9y1TsQ1ufUsFE/jzVyqVyvnPMLVL3s++yB6ne/+133ea65HyuvvHIXAGuQqmfu/vVf/7W8613vKh/4wAe6s1PzetRLI+udFutt7AWqXrs7/p/f689i68mcngtUOa7hVQ1AOKmCBAgMINDrL/EBXsqmQyKQcclfvZzv3e9+d3fJ24s9vvvd75btttuu++tnn322vOc97ykXXHBB98/1e7BqUKqfMaqXCNYbWow8amj5q7/6q1K3r496luvP//zPy6677tp9zmpegWZe+/BigarXfR8JVCuttFIXHOf1qJ8Pq8Gy/qmfs/pf/+t/dbegH+1RbxAyEkTrc52hGk1s0fn7Xn8WW0/m9F6gynENr2oAwkkVJEBgAIFef4kP8FI2HRKBD33oQ+Wf/umfwm5KUc/u1IX/o48+2n2f1d/+7d+WV7/61WWZZZbpbt5Qg9P5558/z5tH1Ev+Lrnkku7v6meZ6l3w6pmaekOH+j1ZI496nNbPfdUzYDVY1bNdNZTVu/vVM2Bzf3ZrrIGq332v72t+gapevlcv4xsJVPVuhp/85Ce7G3HMfnOOee3n2WefPetfC1RDMjQLYDd6/VlsPZnTFIEqxzW8qgEIJ1WQAIEBBHr9JT7AS9l0SATq55Hq55Kibps+Uu+d73xn+drXvvaCd1mDUb3hwmh346tnoo4++ujuzoP1bn51mxd71DNhBx10UBfU6qV0X/3qV0fVndcZqn73vQaq+hjtkr8HH3ywO9tWb5Neb0bR650VBapR27rIPKHXn8XWkzmtF6hyXMOrGoBwUgUJEBhAoNdf4gO8lE2HRKD2/M/+7M/KrbfeOuoX+9bAUL8jauR317xCyXnnnVfe+973lo9+9KPl5JNPnuNd1u+nqrdSr5exjRao6oZPPfVUd2ar3nb9ySefnK9Y/Q6o+l1V9SYb9Rbtoz0i930kUJ1++undZ8Fmf9SzazvvvHNZb731St3H+qjfUVXP2tW7AdabUsx+O/X57bdANVpXF52/7/VnsfVkTu8FqhzX8KoGIJxUQQIEBhDo9Zf4AC9l0yESqJeibbXVVt13O9XvkKo3e6g3RBh51OOiXl73sY99rPuS2vpZpfqYVyi54YYbyhZbbNF9L1P9//UmDPXx2GOPdZ99+o//+I/un2cPVPWW7fXytz/90z+dQ6Ve/rf77rt3QaneibA+6ncz1bsR1u+fmv1Rb0teQ1z9u3qDjdEeUfteX2ckUK211lrd+6vBpz7q92rV76Cq36VVw2Xdv5FHDV71Zhz1ksjPfe5z3XdRzf6od0KsZvXs4chDoBqtq4vO3/f6s9h6Mqf3AlWOa3hVAxBOqiABAgMI9PpLfICXsumQCdTPJNXL9OplafWmEDVgrb766t0X09ZgVP99PVNUvxuphoT6eLEbO+y0007lyiuv7G5rPvIlu/WzTjU01N97I5+TGvm7Gprqv6uffapny+pZqbvvvrv7HFX9stt6s4qR75wa+TxSPdNVz0bVGzfUuxTWUFi3q1+mW/d9tEfUvo8Eqhog6+eh6k0tqs9SSy3V/f8aiur3bk2ZMmWOOxfWM271Rho1WNYbadT39apXvao88MADpZ7Jq2ex6mWMp5xyikA1WjMXwb/v9Wex9WTOQSBQ5biGVzUA4aQKEiAwgECvv8QHeCmbDqFAvSnDF7/4xe5sVL1srt7qu37hbD1DVC9bq2dV6metRh4vFkqmT59ejj/++C4I3Xvvvd3nhuoX1NbbnU+aNKn7Yt7Zz1DVszr1uTXU1UsK//CHP5Q11liju3tfPStWz16NPOq+XXzxxd1nqmroqDeuqPtUw9nHP/7xsu66645JNmrfRwJVvYSvBrtjjjmm1Mse77///u4mGfXLkg877LAu7M39qF9YXC3qGbp6Fqu+72pVLwWsYauevZrd2xmqMbV2kXhSrz+LrSdz2i5Q5biGVzUA4aQKEiAwgECvv8QHeCmbEiBAgMCLCPT6s9h6MudQEqhyXMOrGoBwUgUJEBhAoNdf4gO8lE0JECBAQKAa6mNAoBrq9vzXzglU46RRdpNAIwICVSON9jYJEBhqgV5/FltP5rRToMpxDa9qAMJJFSRAYACBXn+JD/BSNiVAgAABZ6iG+hgQqIa6Pc5QjZP22E0CzQkIVM213BsmQGAIBXr9Wew/0Oc0UaDKcQ2vagDCSRUkQGAAgV5/iQ/wUjYlQIAAAWeohvoYEKiGuj3OUI2T9thNAs0JCFTNtdwbJkBgCAV6/VnsP9DnNFGgynENr2oAwkkVJEBgAIFef4kP8FI2JUCAAAFnqIb6GBCohro9zlCNk/bYTQLNCQhUzbXcGyZAYAgFev1Z7D/Q5zRRoMpxDa9qAMJJFSRAYACBXn+JD/BSNiVAgAABZ6iG+hgQqIa6Pc5QjZP22E0CzQkIVM213BsmQGAIBXr9Wew/0Oc0UaDKcQ2vagDCSRUkQGAAgV5/iQ/wUjYlQIAAAWeohvoYEKiGuj3OUI2T9thNAs0JCFTNtdwbJkBgCAV6/VnsP9DnNFGgynENr2oAwkkVJEBgAIFef4kP8FI2JUCAAAFnqIb6GBCohro9zlCNk/bYTQLNCQhUL97yG2+8sVx55ZVl2rRpZerUqeX+++8vL33pS8uMGTOaO07G6xt+8skny5QpU8rXv/718v3vf7/cfffd5dlnny3rrrtueec731kOOeSQstxyy43Xt9fEfi+22GLd+6w/qxblR68/i/0H+pyjQaDKcQ2vagDCSRUkQGAAgV5/iQ/wUuNu0913371ccsklc+y3QDW+2njGGWeUAw44oNvp+vt3gw02KL/73e/KtddeW5544onyhje8oVx99dVltdVWG19vrKG9HY+Bap999innnHNOueqqq8r2228/pm71+rPYenJMrD0/SaDqmWzhbGAAFo67VyVAYN4Cvf4Sb8nxxBNPLPUMxxZbbNH9eeUrX+kM1Tg7AP75n/+5XH/99eXggw8u66233qy9//Wvf1122WWX8oMf/KD87d/+bTnvvPPG2TsbfHcffPDBUgNnDZQ1XC6//PLd4n+//fYrq6+++uAvEFRBoJo3pPVk0AE2VxmBKsc1vKoBCCdVkACBAQQEqrHj1YWdM1Rj9xr2Z1533XVl66237npaz1q95CUvGfZdDtm/6dOnl4MOOqicffbZZebMmS+oudRSS5V99923nHrqqWXppZcOec1BighUAtUgx0+v2wpUvYotpOcLVAsJ3ssSIDBPAYFq7AeGQDV2q/HwzHr2cdlll+12tX4+bsKECeNhtwfaxxqm3v72t3dnpUZ7bLfdduWyyy4ryyyzzGhPTf37eQWq+lm41772tWVkH48++uhy/vnnlwceeKCstdZa3WWen/jEJ8rItiM7WP/51a9+dfn5z39ejj/++HLuueeWX/3qV+VVr3pV2Wuvvcqhhx76ghD5mte8ptxzzz3z/AzXd7/73fLmN7+57L333l1ArY+5X3N2nLvuuqvUevN69Pqz2Hoy57ATqHJcw6sagHBSBQkQGECg11/iA7zUuN9UoBr3LZzjDfz4xz8uf/Znf1bqGZl6yVs9U7WoPw488MBy+umnj/lt1ud/8YtfHPPzM544v0C11VZblSWWWKLceuutZcstt+xevobFeuOYww47rBx33HFz7FKttfbaa5eNNtqofOtb3yo77rhjd2by29/+dnn88ce7f77iiiu6miOPXgNV/fzUf/7nf5Y777yzvO1tb+suFR55fOYznymveMUrBKqMAyWopkAVBJldRqDKFlafAIFeBASqsWuN10D1XHmuPFoeHfsbHaJnrlJWKYuXxVP2qJ7FqJ8h2m233cq///u/p7zGMBWtZ29qmJjXZX4vtp81bN53330L9TNV8wtUdb8nTpxY/u3f/m1WULnhhhtKDVo1KNXPic1+F8eRWmuuuWYXvF73utd1b/3hhx8uO+ywQ6khu17q+JGPfKTvQFU3dFOKYTrye9sXgao3r4X2bIFqodF7YQIE5iEgUI39sBivgerh8nBZrYzPu9g9VB4qq5ZVx96kMT7zm9/8Ztl1113Lkksu2d1OvZ6xGPZHndV6FqXfRz07Ui9z6/Vx+OGHl0mTJvW62aznv/zlL5/vZXCjFZ5foFp88cXLT3/607L++uvPUeYv//Ivu1vlz32XvZFaX/rSl2bd/XFkw8svv7y7HLLWuu222wSq0RqziP69QDVOGitQjZNG2U0CjQiMNVDVr4B55pnxgbLkkvVzDPH7KlDFm45WMSNQ1QX4m970pvLb3/62nHLKKd0NGsbD47HHHisrrbTSeNjVOfaxOq+44op97/f8AlU9w1QvrZv78bGPfax89rOf7e7eWO/iOPIYqVVvQlLvajj3Y+WVV+6Oi4ceeqisuuofg3yvl/zVbZyh6rvdC31DgWqht2BsOyBQjc3JswgQWDACYw1U9WZg4+UmaE8/XcpSS8X7CVTxpqNVjA5Uv/zlL7swde+993Zf6lsX3ePlIVD91xf7jtyUYtttt53nDTYmT55c6o0qzjrrrC7czB6oaij9zW9+M8+2b7LJJuWHP/xh92fkrKVANV4mJGY/BaoYx/QqAlU6sRcgQKAHgbEGKmeo/nj3rvF423SX/P1xIB555JHu8zY/+9nPutuCn3nmmQNditbDmIU8VaB6YaCqd/mrd9qb+9FvoNp4443LzTffPOZA9Z3vfKe7kcXsd/mr++IMVcghv1CKCFQLhb33FxWoejezBQECeQJjDVR5ezB+Ko/XQOWmFKW7i1+96UC9YcE73vGOcsEFF8xxJ7ctE3SOAAAgAElEQVTxcBT6DFVMoKq9Hu2Sv3ozi9VW++PnDutnqm6//fbuGJr9Bhf1784555wuPAlU42GCxraPAtXYnBb6swSqhd4CO0CAwGwCAtXYD4fxGqjG/g4XzWc+9dRT3c0G6g0K6m2s6x39WvkS39k7uije5a+fM1TVpN46fv/995/jgK+3S995553Leuut131P1cijvsZ//Md/lBtvvLFsuummc2zz3ve+t/uc1tyBauT29FdeeWV5y1veMqbB6vVnsfXkmFh7fpJA1TPZwtnAACwcd69KgMC8BXr9Jd6yo0A1/rr/7LPPlj333LNcdNFF3eV+9U5uL3vZy8bfGwna40Xte6j6DVT1y39rSBr5kt16OWi9dO+WW24pJ598cvnoRz86S/zII48sxx57bPnrv/7r8i//8i+zzmzWLwWuQeq55557QaAaueTwtNNOK//wD/8wpu71+rPYenJMrD0/SaDqmWzhbGAAFo67VyVAQKDq9Ri49NJLu4XUyGPq1KndZ25GvkC0/vsjjjii7LLLLr2W9vwFJFC/U2hkcbzHHnuUFVZYYZ6vPL8vXF1Au7pAXmb69Ond2br6HUyjPWpYqQF06aWXHu2pqX8/v7v89ROo6ndxbbjhhmXk80/1u7bq/6+fUXvzm99cpkyZ0t1Of+RRL/+rXwBdv6uqXv5Xt62XANbvrKrfV1UD2NxnqOrZrC222KI7E1rPeo18me+JJ55YVllllXl6CVSph9GYiwtUY6ZauE8UqBauv1cnQGBOgV5/ibfkd/bZZ3c3L5jfY+67iLXkMx7e68iZgtH29a677pp1tmK05473v6+hqobMeuzO60t+a8Cox30Nows7TFXr6ED16le/uvueqWOOOaa7XO/+++8vEyZMKHvttVc57LDDyjLLLPOCFtcbmXz84x/vgmg9I7XZZpuVemzVfashbO5AVQvU2vUukvU2/dW8PuZ3nPX6s9h6MmcSBaoc1/CqBiCcVEECBAYQ6PWX+AAvZVMCBIZIoJ55qXc6rHfJqzdcqN/LtP3225f99tuvrL766kO0p3G7UgNQDVT1tuvD9uj1Z7H1ZE4HBaoc1/CqBiCcVEECBAYQ6PWX+AAvZVMCBAgsVAGBaqHyj4sXF6jGRZtKEajGSaPsJoFGBASqRhrtbRIg0F2i5wyVA2F+AgLVODk+BKpx0ii7SaARAYGqkUZ7mwQICFSOgVEFBKpRiYbjCQLVcPTBXhAg8EcBgcqRQIAAgYUv0OvPYuvJnJ4JVDmu4VUNQDipggQIDCDQ6y/xAV7KpgQIECDwIgK9/iy2nsw5lASqHNfwqgYgnFRBAgQGEOj1l/gAL2VTAgQIEBCohvoYEKiGuj3/tXMC1ThplN0k0IiAQNVIo71NAgSGWqDXn8XWkzntFKhyXMOrGoBwUgUJEBhAYPZf4qusskpZfPHFB6hmUwIECBDoR6B+YfCjjz7abfqKV7xi1hcav1gt68l+lEffRqAa3WgonmEAhqINdoIAgdkE6i/x+st8xRVXLEsttRQbAgQIEFjAAjNnziyPPfZY9x+16n/cGu1hPTmaUH9/L1D157bAtzIAC5zcCxIgMIrA7373u/LUU0+VZZZZpiy33HK8CBAgQGABC/z+978v06dPLy996UvLCiusMOqrW0+OStTXEwSqvtgW/EYGYMGbe0UCBOYvUMNUDVX1seyyy3a/0F3656ghQIBAvkC9OqD+DP7DH/7QvVgNU/Vn8GgP68nRhPr7e4GqP7cFvpUBWODkXpAAgTEIPPHEE2XGjBljeKanECBAgECGwNJLL12WX375MZW2nhwTU89PEqh6Jls4GxiAhePuVQkQmL9AvTlFDVT1zzPPPIOLAAECBBaQwJJLLllqmKp/FltssTG9qvXkmJh6fpJA1TPZwtnAACwcd69KgMDYBWq48iBAgACBBSMw1hA1+95YT+b0RqDKcQ2vagDCSRUkQIAAAQIECDQlYD2Z026BKsc1vKoBCCdVkAABAgQIECDQlID1ZE67Baoc1/CqBiCcVEECBAgQIECAQFMC1pM57RaoclzDqxqAcFIFCRAgQIAAAQJNCVhP5rRboMpxDa9qAMJJFSRAgAABAgQINCVgPZnTboEqxzW8qgEIJ1WQAAECBAgQINCUgPVkTrsFqhzX8KoGIJxUQQIECBAgQIBAUwLWkzntFqhyXMOrGoBwUgUJECBAgAABAk0JWE/mtFugynENr2oAwkkVJECAAAECBAg0JWA9mdNugSrHNbyqAQgnVZAAAQIECBAg0JSA9WROuwWqHNfwqgYgnFRBAgQIECBAgEBTAtaTOe0WqHJcw6sagHBSBQkQIECAAAECTQlYT+a0W6DKcQ2vagDCSRUkQIAAAQIECDQlYD2Z026BKsc1vKoBCCdVkAABAgQIECDQlID1ZE67Baoc1/CqBiCcVEECBAgQIECAQFMC1pM57RaoclzDqxqAcFIFCRAgQIAAAQJNCVhP5rRboMpxDa9qAMJJFSRAgAABAgQINCVgPZnTboEqxzW8qgEIJ1WQAAECBAgQINCUgPVkTrsFqhzX8KoGIJxUQQIECBAgQIBAUwLWkzntFqhyXMOrGoBwUgUJECBAgAABAk0JWE/mtFugynENr2oAwkkVJECAAAECBAg0JWA9mdNugSrHNbyqAQgnVZAAAQIECBAg0JSA9WROuwWqHNfwqgYgnFRBAgQIECBAgEBTAtaTOe0WqHJcw6sagHBSBQkQIECAAAECTQlYT+a0W6DKcQ2vagDCSRUkQIAAAQIECDQlYD2Z026BKsc1vKoBCCdVkAABAgQIECDQlID1ZE67Baoc1/CqBiCcVEECBAgQIECAQFMC1pM57RaoclzDqxqAcFIFCRAgQIAAAQJNCVhP5rS72UA1Y8aMcsIJJ5Tzzz+/3HvvvWXllVcuO++8cznmmGPKmmuu2ZP25ZdfXk499dTy/e9/vzz++ONlpZVWKltuuWU5+OCDy4477thTrRd7sgEIYVSEAAECBAgQINCsgPVkTuubDFQ1TNWgc+2115YJEyaUiRMnlrvvvrtMmzatrLrqquW6664r66yzzpjETzrppDJp0qSy2GKLlTe96U1ljTXWKL/4xS+6cFUfn//858t//+//fUy15vckAzAwoQIECBAgQIAAgaYFrCdz2t9koDryyCPLscceW7baaqsyZcqUstxyy3W6I+Fo2223LVdfffWo4g8//PCss1nf/va3yzbbbDNrmwsvvLDsueeeZZlllikPPvjgrNcYteiLPMEA9CtnOwIECBAgQIAAgSpgPZlzHDQXqGbOnFlWW2218thjj5WbbrqpbLLJJnPIbrTRRuWWW24pN9xwQ9lss83mq/6Nb3yj7Lbbbt2lgpdddtkLnrvxxhuXm2++uUydOrW7BHCQhwEYRM+2BAgQIECAAAEC1pM5x0Bzgeqqq64qO+ywQ3dJ3x133PEC1Xrmqp7BOuqoo8rkyZPnq37llVeWnXbaadRAdfvtt5d11113oA4agIH4bEyAAAECBAgQaF7AejLnEGguUJ1yyindzSLq5XgXXHDBC1QvvfTSsuuuu5bdd9+9XHTRRfNVr2e5XvOa15Tp06eXuS/5+7d/+7fy13/9191lhd/73vcG7p4BGJhQAQIECBAgQIBA0wLWkzntby5QHXLIIeXkk0/uQlX9zNTcj3qJXr1Ub9NNNy033njjqOpf+9rXynvf+95SLyUcuSnFXXfd1d2Uol4KePbZZ3eXGA76MACDCtqeAAECBAgQINC2gPVkTv+bC1QHHnhgOf3008thhx1WjjvuuBeo1ssA11tvvbL++uuX2267bUzq3/nOd8rf/M3flEceeWTW81dfffXysY99rAtuSyyxxJjq1CeNHOhzb3DnnXd2lyneeuutY67liQQIECBAgAABAgRGBASqnGOhuUB1wAEHlDPOOKMcfvjh3Z3+5n7UzzvVMDXWQPXZz362fOITn+guEayfuXrd617X3Ta9fg7r4osvLu985ztLPYs11odANVYpzyNAgAABAgQIEOhFQKDqRWvsz20uUEVe8ldvrb799tt3lwfWS/wWX3zxWfLPPvts2WKLLcoPfvCDcsUVV3Q3rxjkYQAG0bMtAQIECBAgQICA9WTOMdBcoIq8KcV+++1XvvzlL3eXDtZLCOd+1H9/xBFHlE9+8pPlU5/61EAdNAAD8dmYAAECBAgQINC8gPVkziHQXKAa623T6yV7Rx999HzV3/a2t3VfDPy5z32ufPjDH37Bc+u/P+igg8rf//3fly984QsDddAADMRnYwIECBAgQIBA8wLWkzmHQHOB6umnn+7uuvf444/P94t9p02b1l2yN7/H3nvvXf75n/+5/N3f/V0555xzXvDU973vfeXcc88tJ5xwQvnHf/zHgTpoAAbiszEBAgQIECBAoHkB68mcQ6C5QFUZ6w0pjj/++LL11lt3Z5iWXXbZTrfeRn3SpEllm222Kddcc80s8dNOO63UP3vssUcXjkYe9Xuq3vGOd3R38av/f7fddpv1d5dcckn3d/Xxk5/8pLz+9a8fqIMGYCA+GxMgQIAAAQIEmhewnsw5BJoMVDNmzOhuJjF16tQyYcKEMnHixHLPPfd0/7zKKquU66+/vqy77rqzxOvd++rlf/WMVP1eqZHH888/390u/V//9V+7f7X55puX1772taV+D9UNN9zQ/bsa3A499NCBu2cABiZUgAABAgQIECDQtID1ZE77mwxUlXL69Ond2abzzjuv3HfffWWllVbqvoi33kp9rbXWmkP7xQJVfVINVWeddVZ3yd8tt9xSfv/735cVV1yxbLnllt3nqmrNiIcBiFBUgwABAgQIECDQroD1ZE7vmw1UOZx5VQ1Anq3KBAgQIECAAIEWBKwnc7osUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp93NBqoZM2aUE044oZx//vnl3nvvLSuvvHLZeeedyzHHHFPWXHPNnrXvuOOOcuKJJ5Yrr7yyPPDAA2X55Zcv6623Xtljjz3Kxz/+8Z7rzb2BARiYUAECBAgQIECAQNMC1pM57W8yUNUwteOOO5Zrr722TJgwoUycOLHcfffdZdq0aWXVVVct1113XVlnnXXGLH7RRReV97znPeWpp54qm2yySVl//fXLo48+Wn70ox+VZZddttSwNejDAAwqaHsCBAgQIECAQNsC1pM5/W8yUB155JHl2GOPLVtttVWZMmVKWW655Trdk046qUyaNKlsu+225eqrrx6T+M0331y23HLL7ozUxRdfXLbZZptZ2z333HPlpptuKptvvvmYas3vSQZgYEIFCBAgQIAAAQJNC1hP5rS/uUA1c+bMstpqq5XHHnusCzv1jNLsj4022qjccsst5YYbbiibbbbZqOo1fF1zzTXl61//etl1111HfX6/TzAA/crZjgABAgQIECBAoApYT+YcB80FqquuuqrssMMO3SV987oUr565qmewjjrqqDJ58uT5qv/0pz8tG2ywQXeJ32233ZbTof9f1QCk8ipOgAABAgQIEFjkBawnc1rcXKA65ZRTysEHH1z23HPPcsEFF7xA9dJLL+3ONO2+++6lfjZqfo/TTjutfPjDHy7/8A//UD7zmc+Ur371q92ZrcUWW6xsuOGG5V3veldZYYUVQjpnAEIYFSFAgAABAgQINCtgPZnT+uYC1SGHHFJOPvnkLlTVz0zN/aifidp4443LpptuWm688cb5qn/gAx8oX/jCF8rHPvax7pK/uc9SveIVrygXXnhh95msQR8GYFBB2xMgQIAAAQIE2hawnszpf3OB6sADDyynn356Oeyww8pxxx33AtV6GWC93flYLuN797vf3Z2VWnLJJctKK61UvvjFL3aXEz744IPl6KOPLuedd17372+99dbuboJjeYwc6HM/98477+wuU6y1PAgQIECAAAECBAj0KiBQ9So2tuc3F6gOOOCAcsYZZ5TDDz+8u9Pf3I/bb7+9C1NjCVT1ssGvfe1rXYkrrrii7LTTTnOUq3f/+/73v/+i4W1eLRKoxnbgehYBAgQIECBAgEBvAgJVb15jfXZzgSrykr/3v//95ayzziprrLFG+eUvf/kC889//vPlgx/8YHnTm95U/vM//3OsPZnn8wzAQHw2JkCAAAECBAg0L2A9mXMINBeoIm9Kccwxx3R3A9x6663L9773vRd06LLLLit/8Rd/0V1C+POf/3ygDhqAgfhsTIAAAQIECBBoXsB6MucQaC5QjfW26fXW6fVzUPN71BtR/OVf/mV5wxveUOot1Od+nHvuueV973tf911X9TuvBnkYgEH0bEuAAAECBAgQIGA9mXMMNBeonn766e6LfR9//PH5frHvtGnTyhZbbDFf9SeffLKrVWvWm0astdZaczx///33L2eeeWbZb7/9us9tDfIwAIPo2ZYAAQIECBAgQMB6MucYaC5QVcZ6Q4rjjz++u1RvypQpZdlll+10623UJ02aVLbZZptyzTXXzBKv3zdV/+yxxx7lhBNOmKMTn/zkJ8unP/3psssuu3R3/Bupdfnll5fddtutPPvss+X6668v9QYVgzwMwCB6tiVAgAABAgQIELCezDkGmgxUM2bMKNtvv32ZOnVqdzvziRMnlnvuuaf751VWWaULQOuuu+4s8cmTJ3eX/+29997l7LPPnqMTtdZb3vKW7jNUtdaf//mfl4ceeqir8dxzz3XB7dBDDx24ewZgYEIFCBAgQIAAAQJNC1hP5rS/yUBVKadPn96dbarfFXXfffd13xe18847d7dSn/vSvfkFqlqrXvL3mc98ptTPTP3iF78oSy+9dNl88827Lw+uZ64iHgYgQlENAgQIECBAgEC7AtaTOb1vNlDlcOZVNQB5tioTIECAAAECBFoQsJ7M6bJAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIEEh4dUcAACAASURBVCBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdjcbqGbMmFFOOOGEcv7555d77723rLzyymXnnXcuxxxzTFlzzTX71r799tvLhhtuWGr9t73tbeXyyy/vu9bsGxqAEEZFCBAgQIAAAQLNClhP5rS+yUBVw86OO+5Yrr322jJhwoQyceLEcvfdd5dp06aVVVddtVx33XVlnXXW6Ut8hx12KN/97nfL888/L1D1JWgjAgQIECBAgACBDAGBKkO1lCYD1ZFHHlmOPfbYstVWW5UpU6aU5ZZbrtM96aSTyqRJk8q2225brr766p7FzzzzzLL//vuXAw88sHzpS18SqHoWtAEBAgQIECBAgECWgECVI9tcoJo5c2ZZbbXVymOPPVZuuummsskmm8whu9FGG5Vbbrml3HDDDWWzzTYbs/pDDz1U3vCGN3TbHHbYYeXNb36zQDVmPU8kQIAAAQIECBDIFhCocoSbC1RXXXVVqZfl1Uv67rjjjheo1jNX9QzWUUcdVSZPnjxm9Xe/+93lkksuKT/60Y/KL3/5S4FqzHKeSIAAAQIECBAgsCAEBKoc5eYC1SmnnFIOPvjgsueee5YLLrjgBaqXXnpp2XXXXcvuu+9eLrroojGpf/Ob3yy77LJLd0OLI444ovsMlTNUY6LzJAIECBAgQIAAgQUkIFDlQDcXqA455JBy8sknd6GqfmZq7sfNN99cNt5447LpppuWG2+8cVT1P/zhD6UenEsvvXR3qeBLXvISgWpUNU8gQIAAAQIECBBY0AICVY54c4Gq3jDi9NNP7z7ndNxxx71AtV4GuN5665X111+/3HbbbaOq12BWz3p95zvf6c5K1YczVKOyeQIBAgQIECBAgMACFhCocsCbC1QHHHBAOeOMM8rhhx/e3elv7kf9HqkapsYSqOqNK974xjeW9773veWcc86ZVWqQQDVyoM+9X3feeWf3ua9bb70150hQlQABAgQIECBAYJEWEKhy2ttcoIq65O+ZZ54pW2yxRfelwD/72c+6768aeQhUOQerqgQIECBAgAABAv0LCFT9281vy+YCVdRNKeoXAb/2ta8tr3zlK8vrX//6OYzrLdnrZ7FWWmmlsuGGG3bfc/WNb3xjoA4agIH4bEyAAAECBAgQaF7AejLnEGguUI31tun11ulHH330i6qPBKqxtOXlL395971XgzwMwCB6tiVAgAABAgQIELCezDkGmgtUTz/9dPfFvo8//vh8v9h32rRp3SV9/TwGueTvxV7PAPTTCdsQIECAAAECBAiMCFhP5hwLzQWqylhvSHH88ceXrbfeukyZMqUsu+yynW69jfqkSZPKNttsU6655ppZ4qeddlqpf/bYY49ywgknjNoJgWpUIk8gQIAAAQIECBBYwAICVQ54k4FqxowZZfvtty9Tp04tEyZMKBMnTiz33HNP98+rrLJKuf7668u66647S3zy5Mnd5X977713Ofvss0fthEA1KpEnECBAgAABAgQILGABgSoHvMlAVSmnT5/enW0677zzyn333dfdQGLnnXfubqW+1lprzaEtUOUcfKoSIECAwNgFnn22lOeeG/vzh/mZiy9eyhJLDPMe2jcCi6aAQJXT12YDVQ5nXlUDkGerMgECBIZdoIaplVcu5Xe/G/Y9Hdv+rbBCKb/5jVA1Ni3PIhAnYD0ZZzl7JYEqxzW8qgEIJ1WQAAEC40Zg5sxSXvKSUv7wh1KWWmrc7PY8d7S+l/rR5aefHv/vZXx3wt63KGA9mdN1gSrHNbyqAQgnVZAAAQLjRmAkUC0KIWRRei/j5gCyowT+v4D1ZM6hIFDluIZXNQDhpAoSIEBg3AgsSiFkUXov4+YAsqMEBKrUY0CgSuWNKy5QxVmqRIAAgfEmsCiFkEXpvYy348j+ErCezDkGBKoc1/CqBiCcVEECBAiMG4FFKYQsSu9l3BxAdpSAM1Spx4BAlcobV1ygirNUiQABAuNNYFEKIYvSexlvx5H9JWA9mXMMCFQ5ruFVDUA4qYIECBAYNwKLUghZlN7LuDmA7CgBZ6hSjwGBKpU3rrhAFWepEgECBMabwKIUQhal9zLejiP7S8B6MucYEKhyXMOrGoBwUgUJECAwbgQWpRCyKL2XcXMA2VECzlClHgMCVSpvXHGBKs5SJQIECAyjwDPlmfLb8tt57loNIWu8qpRf3T/+vwx3Xu9lpbJSWbIsOYxtsU8EFikB68mcdgpUOa7hVQ1AOKmCBAgQGBqBc8u55UPlQ+Xx8vjQ7NOC3JGXl5eX08ppZa+y14J8Wa9FoDkB68mclgtUOa7hVQ1AOKmCBAgQGAqBembqFeUVzYapkSbUUPVIecSZqqE4Ku3EoipgPZnTWYEqxzW8qgEIJ1WQAAECQyHwcHm4rFZWG4p9Wdg78VB5qKxaVl3Yu+H1CSyyAtaTOa0VqHJcw6sagHBSBQkQIDAUAgLVf7VBoBqKQ9JOLMIC1pM5zRWoclzDqxqAcFIFCRAgMBQC8wpUPyk/6S4DXJQf9fK+DcoGc7xFgWpR7rj3NgwC1pM5XRCoclzDqxqAcFIFCRAgMBQC8wpULQSLVt/3UBx0dqJZAevJnNYLVDmu4VUNQDipggQIEBgKgVaDRavveygOOjvRrID1ZE7rBaoc1/CqBiCcVEECBAgMhUCrwaLV9z0UB52daFbAejKn9QJVjmt4VQMQTqogAQIEhkKg1WDR6vseioPOTjQrYD2Z03qBKsc1vKoBCCdVkAABAkMh0GqwaPV9D8VBZyeaFbCezGm9QJXjGl7VAISTKkiAAIGhEGg1WLT6vofioLMTzQpYT+a0XqDKcQ2vagDCSRUkQIDAUAi0Gixafd9DcdDZiWYFrCdzWi9Q5biGVzUA4aQKEiBAYCgEWg0Wrb7voTjo7ESzAtaTOa0XqHJcw6sagHBSBQkQIDAUAq0Gi1bf91AcdHaiWQHryZzWC1Q5ruFVDUA4qYIECBAYCoFWg0Wr73soDjo70ayA9WRO6wWqHNfwqgYgnFRBAgQIDIVAq8Gi1fc9FAednWhWwHoyp/UCVY5reFUDEE6qIAECBIZCoNVg0er7HoqDzk40K2A9mdN6gSrHNbyqAQgnVZAAAQJDIdBqsGj1fQ/FQWcnmhWwnsxpvUCV4xpe1QCEkypIgACBoRBoNVi0+r6H4qCzE80KWE/mtF6gynENr2oAwkkVJECAwFAItBosWn3fQ3HQ2YlmBawnc1ovUOW4hlc1AOGkChIgQGAoBFoNFq2+76E46OxEswLWkzmtF6hyXMOrGoBwUgUJECAwFAKtBotW3/dQHHR2olkB68mc1gtUOa7hVQ1AOKmCBAgQGAqBVoNFq+97KA46O9GsgPVkTusFqhzX8KoGIJxUQQIECAyFQKvBotX3PRQHnZ1oVsB6Mqf1AlWOa3hVAxBOqiABAgSGQqDVYNHq+x6Kg85ONCtgPZnTeoEqxzW8qgEIJ1WQAAECQyHQarBo9X0PxUFnJ5oVsJ7Mab1AleMaXtUAhJMqSIAAgaEQaDVYtPq+h+KgsxPNClhP5rReoMpxDa9qAMJJFSRAgMBQCLQaLFp930Nx0NmJZgWsJ3NaL1DluIZXNQDhpAoSIEBgKARaDRatvu+hOOjsRLMC1pM5rReoclzDqxqAcFIFCRAgMBQCrQaLVt/3UBx0dqJZAevJnNYLVDmu4VUNQDipggQIEBgKgVaDRavveygOOjvRrID1ZE7rBaoc1/CqBiCcVEECBAgMhUCrwaLV9z0UB52daFbAejKn9QJVjmt4VQMQTqogAQIEhkKg1WDR6vseioPOTjQrYD2Z03qBKsc1vKoBCCdVkAABAkMh0GqwaPV9D8VBZyeaFbCezGm9QJXjGl7VAISTKkiAAIGhEGg1WLT6vofioLMTzQpYT+a0XqDKcQ2vagDCSRUkQIDAUAi0Gixafd9DcdDZiWYFrCdzWi9Q5biGVzUA4aQKEiBAYCgEWg0Wrb7voTjo7ESzAtaTOa0XqHJcw6sagHBSBQkQIDAUAq0Gi1bf91AcdHaiWQHryZzWC1Q5ruFVDUA4qYIECBAYCoFWg0Wr73soDjo70ayA9WRO6wWqHNfwqgYgnFRBAgQIDIVAq8Gi1fc9FAednWhWwHoyp/UCVY5reFUDEE6qIAECBIZCoNVg0er7HoqDzk40K2A9mdN6gSrHNbyqAQgnVZAAAQJDIdBqsGj1fQ/FQWcnmhWwnsxpvUCV4xpe1QCEkypIgACBoRBoNVi0+r6H4qCzE80KWE/mtF6gynENr2oAwkkVJECAwFAItBosWn3fQ3HQ2YlmBawnc1ovUOW4hlc1AOGkChIgQGAoBFoNFq2+76E46OxEswLWkzmtF6hyXMOrGoBwUgUJECAwFAKtBotW3/dQHHR2olkB68mc1gtUOa7hVQ1AOKmCBAgQGAqBVoNFq+97KA46O9GsgPVkTusFqhzX8KoGIJxUQQIECAyFQKvBotX3PRQHnZ1oVsB6Mqf1AlWOa3hVAxBOqiABAgSGQqDVYNHq+x6Kg85ONCtgPZnTeoEqxzW8qgEIJ1WQAAECQyHQarBo9X0PxUFnJ5oVsJ7Mab1AleMaXtUAhJMqSIAAgaEQaDVYtPq+h+KgsxPNClhP5rReoMpxDa9qAMJJFSRAgMBQCLQaLFp930Nx0NmJZgWsJ3NaL1DluIZXNQDhpAoSIEBgKAQeLA+WV5ZXzrEvt5Zby6pl1aHYv6ydeKQ8UjYoG8xR/qHy0CL/vrM81SUwFgHrybEo9f4cgap3s4WyhQFYKOxelAABAukCPyk/KX9S/iT9dcbDCwhU46FL9nE8C1hP5nRPoMpxDa9qAMJJFSRAgMBQCAhU/9UGgWooDkk7sQgLWE/mNFegynENr2oAwkkVJECAwFAICFR/bMPLy8tLvQxwybLkUPTFThBYFAWsJ3O6KlDluIZXNQDhpAoSIEBgKAQEqj+GqdPKaWWvstdQ9MROEFhUBawnczorUOW4hlc1AOGkChIgQGAoBJ4pz5Sfl5/PsS+rlFXK4mXxodi/BbETK5WVnJlaENBeo3kB68mcQ0CgynENr2oAwkkVJECAAAECBAg0JWA9mdNugSrHNbyqAQgnVZAAAQIECBAg0JSA9WROuwWqHNfwqgYgnFRBAgQIECBAgEBTAtaTOe0WqHJcw6sagHBSBQkQIECAAAECTQlYT+a0W6DKcQ2vagDCSRUkQIAAAQIECDQlYD2Z026BKsc1vKoBCCdVkAABAgQIECDQlID1ZE67Baoc1/CqBiCcVEECBAgQIECAQFMC1pM57RaoclzDqxqAcFIFCRAgQIAAAQJNCVhP5rRboMpxDa9qAMJJFSRAgAABAgQINCVgPZnTboEqxzW8qgEIJ1WQAAECBAgQINCUgPVkTrsFqhzX8KoGIJxUQQIECBAgQIBAUwLWkzntFqhyXMOrGoBwUgUJECBAgAABAk0JWE/mtFugynENr2oAwkkVJECAAAECBAg0JWA9mdNugSrHNbyqAQgnVZAAAQILXeDBBx8sZ5xxRrn66qvLE088UZZffvmy/fbbl/3226+svvrqC33/7AABAouWgPVkTj8FqhzX8KoGIJxUQQIECCw0genTp5eDDjqonH322WXmzJkv2I+lllqq7LvvvuXUU08tSy+99ELbTy9MgMCiJWA9mdNPgSrHNbyqAQgnVZAAAQILRaCGqbe//e3dWanRHtttt1257LLLyjLLLDPaU/09AQIERhWwnhyVqK8nCFR9sS34jQzAgjf3igQIEMgQOPDAA8vpp58+5tL1+V/84hfH/HxPJECAwIsJWE/mHBsCVY5reFUDEE6qIAECBBa4wAMPPFDWXnvteV7m92I7Uy//u++++3ymaoF3ywsSWPQErCdzeipQ5biGVzUA4aQKEiBAYIELHH/88eXwww/v+XXrdoceemjP29mAAAECswtYT+YcDwJVjmt4VQMQTqogAQIEFrjATjvtVK688sqeX/etb31rmTJlSs/b2YAAAQICVf4xIFDlG4e8gkAVwqgIAQIEFqrAVlttVa6//vqe9+GNb3xjue6663rezgYECBAQqPKPAYEq3zjkFQSqEEZFCBAgsFAFnKFaqPxenEDzAtaTOYeAQJXjGl7VAISTKkiAAIEFLuAzVAuc3AsSIDCbgPVkzuEgUOW4hlc1AOGkChIgQGCBC7jL3wIn94IECAhU6ceAQJVOHPMCAlWMoyoECBBY2AK+h2phd8DrE2hXwHoyp/cCVY5reFUDEE6qIAECBBaKwPTp08vb3/72cvXVV4/6+tttt125/PLLy9JLLz3qcz2BAAECowlYT44m1N/fC1T9uS3wrQzAAif3ggQIEEgTqKHqox/9aDnrrLPm+SW/9ct8991333LqqacKU2ldUJhAewLWkzk9F6hyXMOrGoBwUgUJECCw0AUefPDBcuaZZ5bvfve75YknnijLL7982X777ct+++1XVl999YW+f3aAAIFFS8B6MqefAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpd7OBasaMGeWEE04o559/frn33nvLyiuvXHbeeedyzDHHlDXXXHNM2o899lj55je/Wb7xjW+UH/7wh+Wee+4piy++eNlggw3Ke97znvLBD36w1A8WRzwMQISiGgQIECBAgACBdgWsJ3N632SgqmFqxx13LNdee22ZMGFCmThxYrn77rvLtGnTyqqrrlquu+66ss4664wqfvjhh5f6rfc1RG2yySZl3XXXLQ8//HD53ve+V5566qmyzTbblCuuuKK87GUvG7XWaE8wAKMJ+XsCBAgQIECAAIH5CVhP5hwfTQaqI488shx77LFlq622KlOmTCnLLbdcp3vSSSeVSZMmlW233XZM3w/y6U9/ursrUz0TtcYaa8zq0O23317e8pa3dGe+PvnJT5ZPfepTA3fPAAxMqAABAgQIECBAoGkB68mc9jcXqGbOnFlWW221Ui/Xu+mmm7ozS7M/Ntpoo3LLLbeUG264oWy22WZ9q9dLCetlf695zWvKXXfd1XedkQ0NwMCEChAgQIAAAQIEmhawnsxpf3OB6qqrrio77LBDd0nfHXfc8QLVeuaqnsE66qijyuTJk/tW/8lPflLqQfuSl7yku/xv0IcBGFTQ9gQIECBAgACBtgWsJ3P631ygOuWUU8rBBx9c9txzz3LBBRe8QPXSSy8tu+66a9l9993LRRdd1Ld6vVHFbrvtVtZee+3uZhWDPgzAoIK2J0CAAAECBAi0LWA9mdP/5gLVIYccUk4++eQuVNXPTM39uPnmm8vGG29cNt1003LjjTf2rf7Wt761fOtb3yof/vCHy+c+97m+64xsaAAGJlSAAAECBAgQINC0gPVkTvubC1QHHnhgOf3008thhx1WjjvuuBeo1ssA11tvvbL++uuX2267rS/1L3zhC+UDH/hAWXHFFcutt95aXvWqV425zsiBPvcGd955Z3eZYq3nQYAAAQIECBAgQKBXAYGqV7GxPb+5QHXAAQeUM844o9RbntfPS839qHfoq2Gq30B19dVXl5122qnUm19ceOGFZY899hhbJ/7/swSqnrg8mQABAgQIECBAYIwCAtUYoXp8WnOBKvOSv3p3wO222667g2C9zK9e7hf1MABRkuoQIECAAAECBNoUsJ7M6XtzgSrrphT1krz6Rb4PPPBAd3fAepfAyIcBiNRUiwABAgQIECDQnoD1ZE7PmwtUY71ter11+tFHHz0m9fvvv78LU/X7pg466KBSQ1v0wwBEi6pHgAABAgQIEGhLwHoyp9/NBaqnn366+2Lfxx9/fL5f7Dtt2rSyxRZbjKr+29/+tmy77bblxz/+cdl3333LmWeeWRZbbLFRt+v1CQagVzHPJ0CAAAECBAgQmF3AejLneGguUFXGekOK448/vmy99dZlypQpZdlll+10623UJ02a1J1tuuaaa2aJn3baaaX+qTeYOOGEE2b9+yeffLLsuOOO5frrry/vete7ynnnnVeWWGKJlE4ZgBRWRQkQIECAAAECzQhYT+a0uslANWPGjLL99tuXqVOnlgkTJpSJEyd2X75b/3mVVVbpAtK66647S7x+Jqpe/rf33nuXs88+e9a/r99lVS/vqyHqb/7mb8pSSy01zy7Nvk2/bTQA/crZjgABAgQIECBAoApYT+YcB00Gqko5ffr07mxTPat03333lZVWWqnsvPPO3a3U11prrTm0XyxQ7bPPPuWcc84ZtTPPP//8qM8Z7QkGYDQhf0+AAAECBAgQIDA/AevJnOOj2UCVw5lX1QDk2apMgAABAgQIEGhBwHoyp8sCVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA418ph/wAAIABJREFUqYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe1QCEkypIgAABAgQIEGhKwHoyp90CVY5reFUDEE6qIAECBAgQIECgKQHryZx2C1Q5ruFVDUA4qYIECBAgQIAAgaYErCdz2i1Q5biGVzUA4aQKEiBAgAABAgSaErCezGm3QJXjGl7VAISTKkiAAAECBAgQaErAejKn3QJVjmt4VQMQTqogAQIECBAgQKApAevJnHYLVDmu4VUNQDipggQIECBAgACBpgSsJ3PaLVDluIZXNQDhpAoSIECAAAECBJoSsJ7MabdAleMaXtUAhJMqSIAAAQIECBBoSsB6MqfdAlWOa3hVAxBOqiABAgQIECBAoCkB68mcdgtUOa7hVQ1AOKmCBAgQIECAAIGmBKwnc9otUOW4hlc1AOGkChIgQIAAAQIEmhKwnsxpt0CV4xpe9f+1dy/AOlXvA8cfHN3ol9yFptwyNZN7IST3KSJSTcmdbnJNU5FbREbupot7J9eSTCkOJaNcTihChBK55oRR7vGfZ/3nNbyH03vOWvuc933Xd8008/M7e693r8969vuuZ++11+YEcE5KhQgggAACCCCAgFcCjCeD6W4SqmBcndfKCeCclAoRQAABBBBAAAGvBBhPBtPdJFTBuDqvlRPAOSkVIoAAAggggAACXgkwngymu0mognF1XisngHNSKkQAAQQQQAABBLwSYDwZTHeTUAXj6rxWTgDnpFSIAAIIIIAAAgh4JcB4MpjuJqEKxtV5rZwAzkmpEAEEEEAAAQQQ8EqA8WQw3U1CFYyr81o5AZyTUiECCCCAAAIIIOCVAOPJYLqbhCoYV+e1cgI4J6VCBBBAAAEEEEDAKwHGk8F0NwlVMK7Oa+UEcE5KhQgggAACCCCAgFcCjCeD6W4SqmBcndfKCeCclAoRQAABBBBAAAGvBBhPBtPdJFTBuDqvlRPAOSkVIoAAAggggAACXgkwngymu0mognF1XisngHNSKkQAAQQQQAABBLwSYDwZTHeTUAXj6rxWTgDnpFSIAAIIIIAAAgh4JcB4MpjuJqEKxtV5rZwAzkmpEAEEEEAAAQQQ8EqA8WQw3U1CFYyr81o5AZyTUiECCCCAAAIIIOCVAOPJYLqbhCoYV+e1cgI4J6VCBBBAAAEEEEDAKwHGk8F0NwlVMK7Oa+UEcE5KhQgggAACCCCAgFcCjCeD6W4SqmBcndfKCeCclAoRQAABBBBAAAGvBBhPBtPdJFTBuDqvlRPAOSkVIoAAAggggAACXgkwngymu0mognF1XisngHNSKkQAAQQQQAABBLwSYDwZTHeTUAXj6rxWTgDnpFSIAAIIIIAAAgh4JcB4MpjuJqEKxtV5rZwAzkmpEAEEEEAAAQQQ8EqA8WQw3U1CFYyr81o5AZyTUiECCCCAAAIIIOCVAOPJYLqbhCoYV+e1cgI4J6VCBBBAAAEEEEDAKwHGk8F0NwlVMK7Oa+UEcE5KhQgggAACCCCAgFcCjCeD6W4SqmBcndfKCeCclAoRQAABBBBAAAGvBBhPBtPdJFTBuDqvlRPAOSkVIoAAAggggAACXgkwngymu0mognF1XisngHNSKkQAAQQQQAABBLwSYDwZTHd7m1CdOnVKhg4dKrNmzZLdu3dL3rx5pVGjRjJo0CApVqxYurSPHj0qAwYMkPnz58uBAwekcOHC0qxZMxk4cKDkyZMnXXVdbWNOACeMVIIAAggggAACCHgrwHgymK73MqHSZKpu3bqycuVKKVKkiNSsWVN27dolycnJUqBAAVm1apWULFkyIvGUlBSpVq2abN++XUqUKCGVK1eWzZs3m/9KlSolq1evlnz58kVUV1obcQJYE1IBAggggAACCCDgtQDjyWC638uEql+/fvLGG2+YRCgpKUly585tdEeOHCm9evWSWrVqyfLlyyMSb926tSQmJkrz5s1lzpw5kpCQYPbr2rWrjBs3TvTv06dPj6guEiprJipAAAEEEEAAAQQQuIoACVUwoeFdQnX27FkpWLCg6DS99evXS4UKFS6TLVeunGzcuFHWrl0rlSpVSlNdp/cVLVpUcuTIIXv27JFChQpd3P706dNSvHhx+euvv2Tv3r2X/S0jXckJkBE19kEAAQQQQAABBBAICTCeDCYWvEuoli1bJnXq1DFT+nbs2JFKVe9c6R2s/v37m+ei0ipTp06V9u3bm+mDS5cuTbVphw4dZMqUKaLbtW3b1qoHOQGs+NgZAQQQQAABBBDwXoDxZDAh4F1CNXr0aOnRo4e0bNlS5s6dm0p14cKF0rhxY7OohC4ykVbp3r27jBkzRnr37i3Dhw9PtemECROkS5cuotuNGjXKqgc5Aaz42BkBBBBAAAEEEPBegPFkMCHgXULVs2dPk9xoUqXPTIWXDRs2SPny5aVixYqybt26NNX1uSlNujSp0memwsuCBQtMYqbbzZs3z6oHOQGs+NgZAQQQQAABBBDwXoDxZDAh4F1C1blzZ5k4caL06dNHBg8enEpVpwGWLl1aypQpI9u2bUtTvUGDBrJkyRJTX8eOHVNtq9MA69evL7rd4sWLI+rBUKCHb7x161bJmTNnxKsPRvRhbIQAAggggAACCCDgjcDOnTvNePL48ePetDkzGupdQtWpUyeZNGmS9O3b16z0F150+XNNpiJJqDRZ0qRJ69PnpcKLJluaTLlIqLZs2SLZs2eXsmXLZkZc8BlRLqBfiFoiXd4/ypvD4VkKEA+WgHG2O/EQZx1q2RziwRIwznbXC/TZsmWTc+fOxVnLsrY53iVUTPnL2oDj090IcMvejWO81EI8xEtPumkH8eDGMV5qIR7ipSfdtIN4cOMYXot3CRWLUgQTSNSauQJ8IWaud7R/GvEQ7T2UucdHPGSud7R/GvEQ7T2UucdHPATj7V1CFemy6bp0+sCBA9NUj3TZdF06vV27dlY9yAlgxRd3OxMPcdelVg0iHqz44m5n4iHuutSqQcSDFV/c7Uw8BNOl3iVUZ86cMS/2PXbsWJov9k1OTpYqVaqkqb5//34pVqyYJCQkmBf7ar2hEnqxb0pKinmxb+HCha16kBPAii/udiYe4q5LrRpEPFjxxd3OxEPcdalVg4gHK76425l4CKZLvUuolFEXpBgyZIhUr15dkpKSJFeuXEZXl1Hv1auX1KhRQ1asWHFRfPz48aL/PfLIIzJ06NDLeqJVq1YyY8YMadGihcyePdskV1q6desmY8eOFf17YmKide9xAlgTxlUFxENcdad1Y4gHa8K4qoB4iKvutG4M8WBNGFcVEA/BdKeXCdWpU6ekdu3asmbNGilSpIjUrFlTfv/9d/PvfPnyyerVq6VUqVIXxQcMGGCm/7Vp00amTZt2WU8cPnxYqlatKrqKjq64VrlyZdm8ebNs2rTJ/Fvryp8/v3XvcQJYE8ZVBcRDXHWndWOIB2vCuKqAeIir7rRuDPFgTRhXFRAPwXSnlwmVUp48edLcbZo5c6aZrnfzzTdLo0aNzFLqxYsXv0w7rYRKNzxy5Ij0799fPv30Uzl48KAUKlRImjZtapKwvHnzBtNz1IoAAggggAACCCCAAAJZLuBtQpXl8hwAAggggAACCCCAAAIIxLwACVXMdyENQAABBBBAAAEEEEAAgawSIKHKKnk+FwEEEEAAAQQQQAABBGJegIQq5ruQBiCAAAIIIIAAAggggEBWCZBQZZU8n4sAAggggAACCCCAAAIxL0BCFfNdSAMQQAABBBBAAAEEEEAgqwRIqLJAXt+DpUu2z5o1S3bv3m2WVtcl2wcNGiTFihVL1xEdPXpUdFn3+fPny4EDB6Rw4cLSrFkzs2R7njx50lUXG2eNgIt40Dj44osv5PPPP5cff/zRvFcte/bscuedd8qTTz4pzz//vOTMmTNrGsinpkvARTxc6QO3b98ud999t2j9DRs2lEWLFqXruNg4awRcx8OOHTvkrbfekiVLlpjfjBtvvFFKly5tXlzfu3fvrGkkn5ouAZcxod8DY8aMke+//16OHTtmXiFzzz33SI8ePaRu3brpOi42znyBdevWmXM5OTnZvEt13759cu2115rv+YwUxpQZUfv/fUioMm6XoT01yPVLauXKlRdfKrxr1y5zMhQoUEBWrVplXggcSUlJSZFq1aqJDpRKlChx8aXC+mJhfTGxvlRYX1RMiV4BV/HQt29fGTJkiEmiKlSoYPr/zz//lO+++05Onz4tNWrUkMWLF8sNN9wQvRgcmfkRdPX9EM5Zp04d+eabb+TChQskVDESa67jQS+86QUW/U7Q74kyZcqI/o789NNPkitXLtFkixLdAi5jYuTIkdKrVy/Jli2b3HfffVK0aFH59ddfTXKl5Z133pFnn302ukE8Pzq9gL5gwYLLFDKaUDGmtAsmEio7v3Tv3a9fP/PyYE2EkpKSJHfu3KaO0BdbrVq1ZPny5RHV27p1a0lMTJTmzZvLnDlzJCEhwezXtWtXGTdunOjfp0+fHlFdbJQ1Aq7iYdiwYXL8+HFzJ0p/FENFk+169eqZO6GvvvqqvPnmm1nTUD41IgFX8RD+YZMnT5aOHTtK586d5f333yehiqg3sn4jl/GwYcMGc+dB70jpS+j1IkuonD9/XtavX28uylGiW8BVTOgFt9CMmK+++uqyeJg3b560bNlSrr/+ejl48ODFcUp0y/h5dHq3+cSJE1KlShXzn85SymhCxZjSLoZIqOz80rX32bNnpWDBgqK3VPXHS68QXlrKlSsnGzdulLVr10qlSpXSrFunaujAOUeOHLJnzx4pVKjQxe316mPx4sXlr7/+kr179172t3QdMBsHKuAyHtI6UJ1aqlelb7vtNvntt98CbROVZ1wgqHg4dOiQlC1b1nyn9OnTRx544AESqox3U6bt6Toe9GLdihUr5LPPPpPGjRtnWjv4IHcCLmNCp4c3adLEPG7w5ZdfpjrI8uXLiybhOo1ME3FKbAjo3caMJFSMKe37l4TK3jDiGpYtWyY67Uan9F1paoXeudKrT/379zfPRaVVpk6dKu3btzfTg5YuXZpq0w4dOsiUKVNEt2vbtm3Ex8iGmSfgMh7SOuotW7bIXXfdJddcc42Z6kOJToGg4uGJJ54wU0J0Wtcff/xBQhWd3Z/qqFzGw88//2yep9Qpftu2bYsRAQ4zXMBlTOhzNw0aNPjPhEpnOegUckpsCGQ0oWJMad+/JFT2hhHXMHr0aPOgp95Knzt3bqr9Fi5caK4c6pxYneueVunevbt5kFQfIh4+fHiqTSdMmCBdunQR3W7UqFERHyMbZp6Ay3hI66hDVyJvvfVWs1gFJToFgogHXajkoYceMgvevP766+YZKu5QRWf/hx+Vy3gYP368vPjii/LCCy/IiBEjzBRxnQmhgy9dqOSxxx6T//3vf7EB4/FRuowJnSmjsxZOnjwp4VP+PvnkE3n00UfNown6HC4ldgQymlAxprTvYxIqe8OIa+jZs6dJbjSp0memwoveXtfb7BUrVhRduSWtos9NadKlSZU+MxVe9Iq0Jma6nc6HpkSfgMt4SKt19evXN3cxdUA1duzY6IPgiIyA63j4559/zJ3J6667zkwl1juUJFSxE2wu4+G5556Td999V1566SUz5S/8LlX+/PnN74ROC6REr4DLmNBWfvzxx/LUU0+JTiUMLUqh08J1UQqdCjht2jTzmAIldgQymlAxprTvYxIqe8OIa9AHwidOnGieYxg8eHCq/XQaoC5fG8m0DL1Vr7fstT592Dy86ABaB9K6na7uRok+AZfxcLXW6SBKB1O6hL6u/njLLbdEHwRHZARcx4NeuNEr2l9//bW5K6WFhCp2gs1lPOi0z9DCRbos9nvvvWemn+uCA/qKjZkzZ5rlsvU7okiRIrGD5NmRuoyJEJ1+Pzz++ONy+PDhi5r6TLYm3/odos9pU2JHIKMJFWNK+z4mobI3jLiGTp06yaRJk0SXuNbnpcKLzlXWZCqShCp010Hr0+elwktofjQJVcTdk+kbuoyHKx28rhap/a9XH/Xqs75nhhK9Ai7jQadzVa1a1Vx9vnSlTxKq6O3/8CNzGQ86zVzvRmjRC2z6vXBp0UUH9K7E1S72xY5afB+py5hQqbfffltefvllM5tFn9vW16/osun6LLeuBNmiRYuLcRPfsvHTuowmVIwp7WOAhMreMOIaXN6u5/ZsxOxRu6HLeAhvpE7xuv/++82KkjrNT6f7UaJbwFU8nDt3ziyfq0vlb9261bzfLlRIqKI7Bi49OlfxoHXqAkb60LmuDKsLk4QXfd+QvnJBp319++23sYPk2ZG6jAm94Fa7dm3ziIEm0/oOw1D5999/zXfIDz/8cMUE3DP2mGpuRhMqxpT23UxCZW8YcQ0uHyjlAcKI2aN2Q5fxcGkjd+7cad4posug6lVHXTWSEv0CruJBXxR+++23m/eR3HHHHZc1XBNsfVZTp3fpYgT6HjxdtIQSfQKu4kFbpouS6PdA9erVr7jIgC6b/eCDD5op57/88kv0YXBERsBlTIRWAtbHD/TOZHjR/18XsuH9hbEVfBlNqBhT2vczCZW9YcQ1RLrkqd5u13ntaZVIl7jUpdPbtWsX8TGyYeYJuIyH0FHv27fPJFP6YHG3bt3MDzAlNgRcxUMooYqk1TfddJO5i0mJPgFX8aAt04UoHn74YfM+Ml1CPbx8+OGH8vTTT5t3I+o7EinRKeAyJho2bChJSUlXncGgMxv0N+SZZ54xC5pQYkMgowkVY0r7/iWhsjeMuIYzZ86YFXOOHTuW5ot9k5OTze32tMr+/fvNW84TEhLMi30vXYkn9GLflJQU82JfvVJNiT4Bl/GgrTty5IhZpWvTpk0miZ48ebJZFpkSGwKu4+FKrWbKX2zEgh6ly3g4ceKE+Y3QOvUOtr74/dKiCxvp94XetdDncinRKeAyJtq0aSMffPCBtG7d+rLnLEMt1wRbE+2hQ4fKK6+8Ep0gHFUqgYwmVIwp7YOJhMreMF016IIUQ4YMMVMv9OpQrly5zP66jHqvXr3M3QV9m32o6PtD9D9dUEC/2C4trVq1khkzZpgHR2fPnm2SKy16VUmvLunfExMT03V8bJy5Aq7iQQdM+pLn1atXm3fK6KpdrM6UuX3p4tNcxcPVjoWEykUvZV4dLuNBp24NGzbMvJdMV/wL/fYsWrRImjRpIvrcjH5/6AIVlOgVcBUT+toVfW5Gfyf0f2sMhIq+dkX/pkVfDB8+dTh6dTiy/0qoGFMGFyMkVMHZXrHmU6dOmQdB16xZY5anrVmzpnnZqv47X7585gft0reS6zMwOv1PrybpOyEuLbrMqa7kpVccS5YsKZUrVzbL3uodCv231qXvF6FEr4CreAgtka0/jroEbs6cOa/Y6PAYil4ZP4/MVTyQUMVH/LiMB62rXr165hkq/e2599575dChQ+Z34vz58+ZC32uvvRYfcHHcClcxceHCBfNb8dFHHxktHT/os5c6XVxXCdVCTER/IC1cuPCyVaN1LKlJ1aUXRvRZOL2QooUxZXB9SkIVnO1Va9Y3k+vdJr2LoNP19AFxfYmeLqUePhUjreDXD9BpXvqwsS5xqu8U0fdHNG3a1CRhefPmzYLW8ZHpFXARD23btr3itI3wY9EfUUp0C7iIBxKq6O7j9Bydy3jQKWMjRowwU7l0eWx96bMOpPWCTGjAlZ5jY9usEXAVE/p7oM/O6KsVdGXYv//+27yzUAfjujKsjkso0S2gF0n/6zl57WMdI/xXQsWY0q6vSajs/NgbAQQQQAABBBBAAAEEPBYgofK482k6AggggAACCCCAAAII2AmQUNn5sTcCCCCAAAIIIIAAAgh4LEBC5XHn03QEEEAAAQQQQAABBBCwEyChsvNjbwQQQAABBBBAAAEEEPBYgITK486n6QgggAACCCCAAAIIIGAnQEJl58feCCCAAAIIIIAAAggg4LEACZXHnU/TEUAAAQQQQAABBBBAwE6AhMrOj70RQAABBBBAAAEEEEDAYwESKo87n6YjgAACCCCAAAIIIICAnQAJlZ0feyOAAAIIIIAAAggggIDHAiRUHnc+TUcAAQQQQAABBBBAAAE7ARIqOz/2RgABBBBAAAEEEEAAAY8FSKg87nyajgACCCCAAAIIIIAAAnYCJFR2fuyNAAIIIIAAAggggAACHguQUHnc+TQdAQQQQAABBBBAAAEE7ARIqOz82BsBBBBAAAEEEEAAAQQ8FiCh8rjzaToCCCCAAAIIIIAAAgjYCZBQ2fmxNwIIIIAAAggggAACCHgsQELlcefTdAQQQAABBBBAAAEEELATIKGy82NvBBBAAAEEEEAAAQQQ8FiAhMrjzqfpCCCAAAIIIIAAAgggYCdAQmXnx94IIIAAAggggAACCCDgsQAJlcedT9MRQAABBBBAAAEEEEDAToCEys6PvRFAAAEEEEAAAQQQQMBjARIqjzufpiOAAAIIIIAAAggggICdAAmVnR97I4AAAggggAACCCCAgMcCJFQedz5NRwABBBBAAAEEEEAAATsBEio7P/ZGAAEEEEAAAQQQQAABjwVIqDzufJqOAAIIIIAAAggggAACdgIkVHZ+7I0AAggggAACCCCAAAIeC5BQedz5NB0BBBBAAAEEEEAAAQTsBEio7PzYGwEEEEAAAQQQQAABBDwWIKHyuPNpOgIIIIAAAggggAACCNgJkFDZ+bE3AggggAACCCCAAAIIeCxAQuVx59N0BBBAAAEEEEAAAQQQsBMgobLzY28EEEAAAQQQQAABBBDwWICEyuPOp+kIIIAAAggggAACCCBgJ0BCZefH3ggggAACCCCAAAIIIOCxAAmVx51P0xFAAAEEEEAAAQQQQMBOgITKzo+9EUAAAQQQQAABBBBAwGMBEiqPO5+mI4AAAggggAACCCCAgJ0ACZWdH3sjgAACCCCAAAIIIICAxwIkVB53Pk1HAAEEEEAAAQQQQAABOwESKjs/9kYAAQQQQAABBBBAAAGPBUioPO58mo4AAggggAACCCCAAAK6NnxLAAAAg0lEQVR2AiRUdn7sjQACCCCAAAIIIIAAAh4LkFB53Pk0HQEEEEAAAQQQQAABBOwESKjs/NgbAQQQQAABBBBAAAEEPBYgofK482k6AggggAACCCCAAAII2AmQUNn5sTcCCCCAAAIIIIAAAgh4LEBC5XHn03QEEEAAAQQQQAABBBCwE/g/UvfJnVVku8gAAAAASUVORK5CYII=\" width=\"568\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knefmnn_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, \"2D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using parallel coordinates. This mode best fits for any dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a parallel coordinates graph\n",
    "knefmnn_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, file_path=\"par_cord/knefmnn_par_cord.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"820\"\n",
       "            height=\"520\"\n",
       "            src=\"https://uts-caslab.github.io/hyperbox-brain/docs/tutorials/par_cord/knefmnn_par_cord.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2da0fabd518>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load parallel coordinates to display on the notebook\n",
    "from IPython.display import IFrame\n",
    "# We load the parallel coordinates from GitHub here for demostration in readthedocs\n",
    "# On the local notebook, we only need to load from the graph storing at 'par_cord/knefmnn_par_cord.html'\n",
    "IFrame('https://uts-caslab.github.io/hyperbox-brain/docs/tutorials/par_cord/knefmnn_par_cord.html', width=820, height=520)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}